Matriculation number: 30496A

Introduction

Objective

The primary aim of this project is to conduct a comprehensive analysis of IMDb scores and ratings, age certification ratings, and the evolution of content quality over time for TV shows and movies on Netflix. This multifaceted approach serves several key objectives.

Analysis of IMDb Scores

  • Goal: To identify prevailing trends and patterns in audience preferences.
  • Importance:
    • Provides valuable insights for content creators and producers on investment-worthy shows or movies.
    • Sheds light on factors contributing to a show’s or movie’s success on Netflix.

Examination of Age Certification Ratings

  • Goal: To understand the target audience for different TV shows and movies on Netflix.
  • Relevance:
    • Offers critical information for advertisers targeting specific demographic groups.
    • Serves as a resource for parents and guardians in selecting appropriate content for children.

Evolution of Content Quality Over Time

  • Goal: To explore how the quality of content on Netflix has evolved over the years.
  • Approach: Comparing IMDb scores and votes across different release years.
  • Expected Insights:
    • Potential shifts in audience preferences and industry standards.
    • Influences on viewers’ perceptions and opinions over time.

Data used

Source of Data

The dataset used in this analysis was obtained from Back 2 Viz Basics, which provides comprehensive information about various titles available on the popular streaming platform. The dataset includes details such as the title’s name, its type (whether it is a TV show or a movie), a brief description of the content, the year it was released, age certification rating, runtime (for TV shows: length of episodes; for movies: duration), IMDb score, and IMDb votes. The dataset is publicly available and was downloaded for the purpose of this project.

Structure of the Dataset

-The dataset consists of [5283] rows and [11] columns.

The dataset includes the following variables:

  • index: An integer representing the index of each record.
  • id: A character identifier for each record.
  • title: A character representing the title of the movie or TV show.
  • type: A character indicating whether it’s a “MOVIE” or a “SHOW.”
  • description: A character containing a brief description of the movie or TV show.
  • release_year: An integer indicating the year of release.
  • age_certification: A character specifying the age certification (e.g., “R,” “PG”).
  • runtime: An integer representing the duration in minutes.
  • imdb_id: A character containing the IMDb ID for the movie or TV show.
  • imdb_score: A numeric value representing the IMDb score.
  • imdb_votes: A numeric value indicating the number of IMDb votes.

The data was provided in CSV format and was imported into R for analysis. Prior to analysis, some data cleaning and preprocessing steps were performed to handle missing values and ensure data consistency.

Methodology

Data Collection and Preparation

Load Necessary Libraries

library(tidyverse)
library(ggplot2)
library(dplyr)
library(plotly)
library(htmlwidgets)
library(knitr)

Load the new cleaned dataset

netflix_data <- read.csv("D:/study/coding for data science/Netflix_Imdb_Project/Netflix_IMDB_Scores/Data/processed_data/cleaned_netflix_data.csv")

Display the first few rows

head(netflix_data)
##   index       id                           title  type
## 1     0  tm84618                     Taxi Driver MOVIE
## 2     1 tm127384 Monty Python and the Holy Grail MOVIE
## 3     2  tm70993                   Life of Brian MOVIE
## 4     3 tm190788                    The Exorcist MOVIE
## 5     4  ts22164    Monty Python's Flying Circus  SHOW
## 6     5  tm14873                     Dirty Harry MOVIE
##                                                                                                                                                                                                                                                                                                                                                                                                                                                          description
## 1                                                                                                                                                                                                                                A mentally unstable Vietnam War veteran works as a night-time taxi driver in New York City where the perceived decadence and sleaze feed his urge for violent action, attempting to save a preadolescent prostitute in the process.
## 2                                    King Arthur, accompanied by his squire, recruits his Knights of the Round Table, including Sir Bedevere the Wise, Sir Lancelot the Brave, Sir Robin the Not-Quite-So-Brave-As-Sir-Lancelot and Sir Galahad the Pure. On the way, Arthur battles the Black Knight who, despite having had all his limbs chopped off, insists he can still fight. They reach Camelot, but Arthur decides not  to enter, as "it is a silly place".
## 3 Brian Cohen is an average young Jewish man, but through a series of ridiculous events, he gains a reputation as the Messiah. When he's not dodging his followers or being scolded by his shrill mother, the hapless Brian has to contend with the pompous Pontius Pilate and acronym-obsessed members of a separatist movement. Rife with Monty Python's signature absurdity, the tale finds Brian's life paralleling Biblical lore, albeit with many more laughs.
## 4                                                                                                                12-year-old Regan MacNeil begins to adapt an explicit new personality as strange events befall the local area of Georgetown. Her mother becomes torn between science and superstition in a desperate bid to save her daughter, and ultimately turns to her last hope: Father Damien Karras, a troubled priest who is struggling with his own faith.
## 5                                                                                                                                                                                                                                                                                            A British sketch comedy series with the shows being composed of surreality, risqué or innuendo-laden humour, sight gags and observational sketches without punchlines.
## 6                                                                When a madman dubbed 'Scorpio' terrorizes San Francisco, hard-nosed cop, Harry Callahan – famous for his take-no-prisoners approach to law enforcement – is tasked with hunting down the psychopath. Harry eventually collars Scorpio in the process of rescuing a kidnap victim, only to see him walk on technicalities. Now, the maverick detective is determined to nail the maniac himself.
##   release_year age_certification runtime   imdb_id imdb_score imdb_votes
## 1         1976                 R     113 tt0075314        8.3     795222
## 2         1975                PG      91 tt0071853        8.2     530877
## 3         1979                 R      94 tt0079470        8.0     392419
## 4         1973                 R     133 tt0070047        8.1     391942
## 5         1969             TV-14      30 tt0063929        8.8      72895
## 6         1971                 R     102 tt0066999        7.7     153463

Summary statistics

knitr::kable(summary(netflix_data))
index id title type description release_year age_certification runtime imdb_id imdb_score imdb_votes
Min. : 0 Length:5283 Length:5283 Length:5283 Length:5283 Min. :1953 Length:5283 Min. : 0.0 Length:5283 Min. :1.500 Length:5283
1st Qu.:1320 Class :character Class :character Class :character Class :character 1st Qu.:2015 Class :character 1st Qu.: 45.0 Class :character 1st Qu.:5.800 Class :character
Median :2641 Mode :character Mode :character Mode :character Mode :character Median :2018 Mode :character Median : 87.0 Mode :character Median :6.600 Mode :character
Mean :2641 NA NA NA NA Mean :2016 NA Mean : 79.2 NA Mean :6.533 NA
3rd Qu.:3962 NA NA NA NA 3rd Qu.:2020 NA 3rd Qu.:106.0 NA 3rd Qu.:7.400 NA
Max. :5282 NA NA NA NA Max. :2022 NA Max. :235.0 NA Max. :9.600 NA

Information about columns and data types

str(netflix_data)
## 'data.frame':    5283 obs. of  11 variables:
##  $ index            : int  0 1 2 3 4 5 6 7 8 9 ...
##  $ id               : chr  "tm84618" "tm127384" "tm70993" "tm190788" ...
##  $ title            : chr  "Taxi Driver" "Monty Python and the Holy Grail" "Life of Brian" "The Exorcist" ...
##  $ type             : chr  "MOVIE" "MOVIE" "MOVIE" "MOVIE" ...
##  $ description      : chr  "A mentally unstable Vietnam War veteran works as a night-time taxi driver in New York City where the perceived "| __truncated__ "King Arthur, accompanied by his squire, recruits his Knights of the Round Table, including Sir Bedevere the Wis"| __truncated__ "Brian Cohen is an average young Jewish man, but through a series of ridiculous events, he gains a reputation as"| __truncated__ "12-year-old Regan MacNeil begins to adapt an explicit new personality as strange events befall the local area o"| __truncated__ ...
##  $ release_year     : int  1976 1975 1979 1973 1969 1971 1964 1980 1967 1966 ...
##  $ age_certification: chr  "R" "PG" "R" "R" ...
##  $ runtime          : int  113 91 94 133 30 102 170 104 110 117 ...
##  $ imdb_id          : chr  "tt0075314" "tt0071853" "tt0079470" "tt0070047" ...
##  $ imdb_score       : num  8.3 8.2 8 8.1 8.8 7.7 7.8 5.8 7.7 7.3 ...
##  $ imdb_votes       : chr  "795222" "530877" "392419" "391942" ...

Check for missing values

knitr::kable(colSums(is.na(netflix_data)))
x
index 0
id 0
title 0
type 0
description 0
release_year 0
age_certification 0
runtime 0
imdb_id 0
imdb_score 0
imdb_votes 0

Results

Analysis of IMDb Scores

fig_1a - Histogram of IMDb Scores: This visualization separates the IMDb scores by content type (movies or shows) and displays their distribution. It is a direct approach to analyzing audience rating trends and can show if certain scores are more common for either type of content.

fig_1a <- ggplot(netflix_data, aes(x = imdb_score, fill = type)) +
  geom_histogram(binwidth = 0.5, position = "dodge", alpha = 0.7) +
  labs(title = "Distribution of IMDb Scores on Netflix",
       x = "IMDb Scores",
       y = "Number of Titles") +
  theme_minimal()
fig_1a

Distribution of Types

fig_1b - Pie Chart of Content Types: This chart gives a quick visual representation of the proportion of movies to TV shows in the dataset, although it doesn’t directly relate to IMDb scores.

type_counts <- table(netflix_data$type)

fig_1b <- plot_ly(
  labels = names(type_counts),
  values = as.vector(type_counts),
  type = "pie"
) %>%
  layout(title = "Distribution of Types")
fig_1b

IMDb Scores vs. Runtime

fig_1c - Scatter Plot of IMDb Scores vs. Runtime: This plot can help to identify if there’s a relationship between the length of content and its IMDb rating, which can be a factor in content investment decisions.

fig_1c <- ggplot(netflix_data, aes(x = runtime, y = imdb_score)) +
  geom_point() +
  labs(title = "IMDb Scores vs. Runtime",
       x = "Runtime",
       y = "IMDb Score") +
  theme_minimal()
fig_1c

Average IMDb Score by Type

fig_1d - Average IMDb Score by Type Bar Chart: This shows whether movies or TV shows have higher average ratings, which is directly useful for content creators and producers.

fig_1d <- netflix_data %>%
  group_by(type) %>%
  summarise(average_score = mean(imdb_score, na.rm = TRUE)) %>%
  ggplot(aes(x = type, y = average_score, fill = type)) +
  geom_bar(stat = "identity") +
  labs(title = "Average IMDb Score by Type", x = "Type", y = "Average IMDb Score")
fig_1d

Boxplot of IMDb Scores by Type

fig_1e - Boxplot of IMDb Scores by Type: This provides insights into the spread and distribution of ratings for movies and shows, including median scores and outliers.

fig_1e <- plot_ly(netflix_data, x = ~type, y = ~imdb_score, type = 'box'
) %>%
  layout(title = 'Boxplot of IMDb Scores by Type',
         xaxis = list(title = 'Type'),
         yaxis = list(title = 'IMDb Score'))
fig_1e

Top Average IMDb Scores

fig_1f - Treemap of Top IMDb Scores: This highlights specific titles with scores and votes around a chosen threshold, which can show which high-rated titles are in this score range, but it may be somewhat arbitrary depending on the choice of threshold and margin.

threshold_score <- 7.1
margin_of_error <- 0.3 


suppressWarnings({
  netflix_data$imdb_votes <- as.numeric(as.character(netflix_data$imdb_votes))
  netflix_data$imdb_score <- as.numeric(as.character(netflix_data$imdb_score))
})

top_scores <- netflix_data %>%
  filter(between(imdb_score, threshold_score - margin_of_error, threshold_score + margin_of_error)) %>%
  arrange(desc(imdb_votes), desc(imdb_score)) %>%
  head(16) %>%
  mutate(hover_text = paste("Year:", release_year, 
                            "<br>Type:", type, 
                            "<br>IMDb Score:", imdb_score, 
                            "<br>Votes:", format(imdb_votes, big.mark = ",")))

# Create the treemap plot
fig_1f <- plot_ly(
  data = top_scores,
  labels = ~title,
  parents = rep("", nrow(top_scores)),
  type = 'treemap',
  hoverinfo = 'text',
  hovertemplate = '%{customdata}<extra></extra>',
  customdata = ~hover_text,
  textfont = list(size = 16)
) %>%
  layout(
    title = "Top Average IMDb Scores by Vote Count within Score Range",
    margin = list(l = 10, r = 10, b = 10, t = 40)
  )

fig_1f

Examination of Age Certification Ratings

Target Audience Analysis by Age Certification and Content Type

fig_2a - Target Audience Analysis:This visualization provides a comparative analysis of the number of Netflix titles across different age certifications, separated by content type (Movie or Show). By utilizing a bar chart, the graph distinctly illustrates the count of titles for each age certification category, with a clear differentiation between movies and shows.

audience_target_analysis <- netflix_data %>%
  group_by(age_certification, type) %>%
  summarise(count = n(), .groups = 'drop') %>%
  arrange(desc(count))

# Visualize the number of titles targeting each age certification, split by content type
fig_2a <-  ggplot(audience_target_analysis, aes(x = age_certification, y = count, fill = type)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Target Audience Analysis by Age Certification and Content Type",
       x = "Age Certification", y = "Number of Titles",
       fill = "Content Type") +
  theme_minimal()
fig_2a

Ratings by Age Certification

fig_2b - IMDb Scores by Age Certification: The fig_2b visualization presents a detailed analysis of IMDb scores categorized by age certification for the content available on Netflix. This is achieved through a series of boxplots, each representing a different age certification category.

fig_2b <- ggplot(netflix_data, aes(x = age_certification, y = imdb_score, fill = age_certification)) +
  geom_boxplot() +
  labs(title = "IMDb Scores by Age Certification", x = "Age Certification", y = "IMDb Score")
fig_2b

Evolution of Content Quality Over Time

Relationship Between Scores and Votes:

fig_3b - IMDb Scores vs. IMDb Votes Scatter Plot: This scatter plot compares the IMDb scores with the number of votes they’ve received, which might indicate the popularity and audience engagement with titles of different quality. Using a logarithmic scale for votes takes into account the wide range of vote counts.

fig_3b <- plot_ly(netflix_data, x = ~imdb_votes, y = ~imdb_score, type = 'scatter', mode = 'markers',
                  marker = list(size = 5, opacity = 0.5)) %>%
  layout(title = 'IMDb Scores vs. IMDb Votes',
         xaxis = list(title = 'IMDb Votes', type = "log"), # Using log scale for X-axis
         yaxis = list(title = 'IMDb Score'))
fig_3b
## Warning: Ignoring 16 observations

IMDB Scores Over the Years by Type

fig_3c - IMDb Scores Over the Years by Type: By grouping the data by content type (movies or shows) and plotting IMDb scores over time, you can compare how movies and shows might differ in terms of quality trends. This could reveal if there are distinct patterns in audience perception based on the type of content.

fig_3c <- ggplot(netflix_data, aes(x = release_year, y = imdb_score, group = type, color = type)) +
  geom_line(aes(group = interaction(release_year, type)), alpha = 0.3) +  # Use interaction for grouping in geom_line
  geom_smooth(se = FALSE, method = "loess" , formula = y ~ x) +  # Add LOESS smoothed trend line
  labs(title = "IMDb Scores Over the Years by Type", x = "Release Year", y = "IMDb Score") +
  theme_minimal() 
fig_3c

Conclusion

In our analysis of Netflix’s content library, we focused on evaluating IMDb scores and ratings, age certification distributions, and the evolution of content quality over time. Using a detailed dataset, we revealed key trends in audience preferences, essential for content creators and strategists.

Our investigation highlighted how different content types and age certifications are received by diverse audiences. For instance, the age certification analysis provided insights into Netflix’s target demographics, offering valuable information for advertisers and content creators. The trend analysis of IMDb scores over the years indicated shifts in content quality and audience expectations, reflecting evolving industry standards and viewer tastes.

Overall, our study presents a snapshot of Netflix’s dynamic content strategy, offering critical insights for stakeholders in the digital entertainment industry. As the platform continues to expand and diversify, such data-driven analyses are crucial in understanding and adapting to the changing landscape of viewer preferences.